Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Budget-Constrained Coalition Strategies with Discounting
Authors: Lia Bozzone, Pavel Naumov
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper proposes a sound and complete logical system for reasoning about budget-constrained strategic abilities that incorporates discounting into its semantics. |
| Researcher Affiliation | Academia | Lia Bozzone1 and Pavel Naumov2 1Vassar College 2King s College EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not use or provide access to any datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not mention any specific dataset split information (e.g., training, validation, test splits). |
| Hardware Specification | No | The paper does not describe any experiments and therefore provides no specific hardware details used for running them. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific ancillary software details or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details or hyperparameters. |